Challenge: Is your data clean, correct and useful?
As 5G and IoT data become more mainstream, companies are continually seeing the exponential growth in their data. Companies need to ensure their data assets are accurate and reliable so that they can optimise digital initiatives, strengthen competitive standing and leads to business success. However, manual checking and spot checking of data is time consuming, inefficient and always prone to errors.
Impact: Poor quality data undermines your business
Gartner estimates that the cost of data to organizations has reached US$15 million per annum, threatening regulatory compliance and customer trust. Poor quality data undermines their digital initiatives and weakens their competitive standing and leads to customer distrust. The BDM Data Validation module enables you to record, measure and apply data validation checks, as it moves within your data lake
Solution: Completeness and Integrity Checks
BDM carries out two distinct checks on data: Completeness and Integrity. These checks are carried out on the data as it is being moved between source and destination i.e. when it is in memory in the Spark cluster. This methodology is secure and optimal as it ensures that replicas of the data are not written to disk.
Depending on the results of the validation check it is possible to:
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Remove records that fail the validation and continue processing the source data
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Continue processing the data with failed records, but move a copy of the record into a separate error log file
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Fail the job
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Send an alert
Opportunity: Produce clean, reliable Data
High-quality data is essential to business intelligence efforts and data analytics, as well as better operational efficiency. With trusted and accurate data, your data scientists can increase productivity as less time spent trying to fix data. Your data science team can now deliver trusted data to accelerate actionable insights at speed.
